id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_oa2gtpcm7vdbji3sjl376w7e2u	Ferhat Ozgur Catak	Data augmentation based malware detection using convolutional neural networks	2021	26	.pdf	application/pdf	9317	1298	55	this problem, we created variants of the images by applying data augmentation methods. the collected malware samples are converted into binary file to 3-channel images This study uses five different deep CNN model for malware family detection. Keywords Convolutional neural networks, Cybersecurity, Image augmentation, Malware analysis based on creating a grayscale image from malware code and then using classification family classification model that exploits augmentation for malware variants and takes Herein, we demonstrate that the data augmentation-based 3-channel image classification from these studies because we used five different deep CNN models for malware family is applying data augmentation enhanced malware family classification model. samples according their family using malware images based CNN model. malware detection model with the best classification performance. malware environment using an image augmentation enhanced deep CNN model. Imbalanced malware images classification: a cnn based approach. Data augmentation based malware detection using convolutional neural networks Data augmentation based malware detection using convolutional neural networks	./cache/work_oa2gtpcm7vdbji3sjl376w7e2u.pdf	./txt/work_oa2gtpcm7vdbji3sjl376w7e2u.txt